Information systems that use knowledge base must be able to organize, store, access and manage that knowledge base in an efficient way. Large scale applications such as web search require web scale knowledge base. Organizing, storing, accessing and managing such a web scale knowledge base will need a scalable framework that
minimizes or eliminates complex syntax requirements for inputting knowledge
provides guidelines or discipline for knowledge modeling
enables collaboration of many knowledge contributors
To elaborate the challenge of complex syntax requirements, one of the techniques for capturing knowledge is Ontologies. Ontologies are captured using formats such as RDF, DAML, OWL and Topic Maps. All these languages to capture the ontologies have their own syntaxes and rules to build knowledge base. However, they all use XML as underlying language and hence are plagued with problems of XML such as multiple possibilities of representations.
To understand the need for discipline or guideline, consider the following example. To capture knowledge of the automobile domain, one could describe Vehicle as a Concept and Car as its instance, whereas others could capture Vehicle as Concept and Car as its child. Which is correct representation? The inventors of the present invention have determined that the second representation is more appropriate as it will allow user to extend it better for future concepts like make, model and trim. Therefore, it will be beneficial to provide user some discipline or guidelines for knowledge modelers to collaborate on knowledge base.
Lastly, if collaboration is the key to building a web scale knowledge base, then the inventors of the present invention have determined that another important requirement of a knowledge framework is that it should be such that a human being, who adds the knowledge as well as a machine that is going to use it, should be able to understand it easily. In the field of artificial intelligence, logic based representations are used to capture knowledge. Rationale behind it lies in the expressivity of mathematical logic. However, even though logic based representations are concise and expressive enough for machines to use, the syntax and structure they use are difficult for a typical web user or an average knowledge contributor to add more knowledge easily, thus discounting the possibility of web community participating in building the knowledge base. Unlike artificial intelligence approach, Ontologies do allow the use of natural language to describe knowledge making it easier for human input. This application builds on the strengths of the ontology based approach to enable even a typical web user or an average knowledge contributor to collaborate on building and managing a web scale knowledge base.
Furthermore, as recognized by the inventors of the present invention, how the eventual knowledge base is envisioned is the key to organizing and building it. Knowledge modelers and practitioners in the field of semantic web envision web scale knowledge base in at least two ways: (1) as one big connected model as does Freebase.com which tries to capture to knowledge of the web and (2) as a collection of bits and pieces of information rather than one big fully connected graph. The inventors of the present invention have determined that the later approach is key to collaboratively building a web scale knowledge base where contributors can add knowledge base incrementally in bits and pieces without having to always crosscheck its impact on the existing knowledge base. The inventors recognize that it is the job of knowledge based system such as a search engine to connect the bits and pieces of information on demand and based on context.
Therefore, it would be desirable to provide a method and apparatus that overcomes these noted drawbacks of the prior art and provides an improved framework for web scale knowledge base.